Suppose you are a marketer at a wireless telecommunications company in Europe. Your company has a deep historical record of customer transactions, products purchased and billing history. Three months ago, your company also finished an implementation of an activity based costing project complemented with a profitability management application that now shows customer spend, margins and life-time value.

Deciding it’s high time to determine which of your customers are creating and potentially destroying value, you start analyzing a key segment of customers.

On one particular day, while finishing the last sip of your Monster energy drink, you’ve zeroed in on customer “Thomas Smith”.
The analysis shows that Mr. Smith is chronically late on his bills. He does pay but often late, and while you appreciate the incremental revenues associated with late charges, you also examine that he constantly uses your call center to ask mundane questions (bypassing the automated systems). You also see from your product analysis that he likes to switch phones frequently, bringing back product just before the thirty day exchange policy expires.

In the initial analysis, it appears Mr. Smith is costing your company a lot of money. Now, as a marketer, should you keep Mr. Smith as a customer, or gently pass him onto the competition?

What might be obvious is in fact a very complex decision, especially when you include social network analysis.

Leading edge companies are using Social Network Analysis to detect and interpret the patterns of social ties within a customer base. Authors Stanley Wasserman and Katherine Faust in their book “Social Network Analysis: Methods and Applications“, mention that SNA is “based on an assumption of the importance of relationships among interacting units. The social network encompasses theories, models, and applications that are expressed in terms of relational concepts or processes.”

In this particular marketing example, social network analysis can be used to determine the “importance” of Mr. Smith, especially in relation to other paying customers.

So let’s get back to the analysis on Thomas Smith. By adding call detail records to your data warehouse, and using social network analysis techniques, you now see that Mr. Smith is a “node” in a pretty complex network of customers. In fact, through a “Fab Five” campaign you concocted a year ago (where customers can call five friends for free in-network), you also see that Thomas is linked to five very profitable customers.

Since Mr. Smith is well connected to five very profitable customers, treating him poorly on his next customer visit, or jettisoning Mr. Smith altogether could lead to the defection of his five closest friends … customers on your network, and customers that pay their bills and produce positive cash flow.

Let us suppose you also had the divine prescience to add net promoter scoring to the mix. Now you notice that while Mr. Smith has trouble paying his bills, he is in fact a “promoter” of your company. He likes your friendly customer service representatives, and also is very generous in telling his friends about your willingness to “bend over backwards for him” to meet his changing needs.

With this analysis in hand, you determine that Mr. Smith isn’t a customer to jettison, and in fact, is probably one that deserves a closer look.

Before we get too caught up in the hype, social network analysis isn’t a savior to marketing decision making.

Social network analysis–done right–requires a lot of data from myriad sources. In the hypothetical marketing example above, simple call detail records are used, but to say–find a terrorist–the National Security Agency of the United States (NSA) would require call detail records, credit card transactions, car rental receipts, and many other digital markers. Even then, the output of the analysis isn’t always accurate.

Accurate data is a key factor in reliable results, but so too are the assumptions used in the model. Is Mr. Smith really that “important”? Should we assume that “associations” are actual close relationships? Should we also assume that if Mr. Smith leaves our company, his “Fab Five” connections will follow?

Social network analysis–hype or help? It’s up to you to decide.

Questions:
* It is well documented that Social Network Analysis (SNA) can be the source of “false positives”. Would you trust the output of SNA to make marketing decisions in your organization?
* Call detail records show Mr. Smith calls five contacts quite often. Does “activity” … in this instance a phone call–denote a “close relationship”?
* SNA can also be used to model employee connections in an organization–in effect to determine the “importance” of an employee? Good or bad idea?
* If you were the marketer in the situation above, what other tools might you use to determine if you should keep Mr. Smith as a customer?

The proliferation of mobile and GPS technologies (sometimes in the same handset), are making it possible for city planners, government officials, and even businesses to gain a pulse of the daily movements of entire populations.

A recent article in the Wall Street Journal, “Cellphone Data Track Our Migration Patterns“, June 10, 2008, mentions how mobile providers are allowing access of anonymized and aggregated location data to social scientists, physicists and urban planners.

The article notes, “More than 3.3 billion wireless-phone subscribers world-wide have, in effect, voluntarily adopted devices that record their daily movements in the same way satellite sensors monitor migrating birds, whales, bears and other wildlife.”

Indeed, network physicist Albert-Laszlo Barabasi says that since practically everyone has a mobile phone, “Everything we do leaves an electronic fingerprint somewhere.”

While the concept of tracking human movement raises the eyebrows of many privacy advocates, the benefits to exploratory analysis of GPS and mobile data–according to the article–are myriad including, “aid(ing) emergency relief efforts in natural disasters, as well as improving urban planning, public transportation and traffic control.”

The Wall Street Journal article also mentions another case study where MIT and Telecom Italia have teamed through Project “Real Time Rome” to help its citizens and officials make better decisions regarding resource utilization.

Overlaying telecommunications data and Google Earth allows Project “Real Time Rome” to dynamically reveal “the rhythm of the city” through six visualizations:

* Pulse …. helps determine commuting patterns and patterns of use for transportation networks
* Connectivity–ensures public transportation is located near populations
* Flow–helps answer the question, “Where is traffic moving or flowing to?”
* Icons– helps answer the question, “Which landmarks in Rome attract more people”?
* Visitors–discovers where tourists congregate
* Gathering–during special events (i.e. Madonna concert), helps determine how people occupy and move through different parts of Rome

Sophisticated analytical applications and data warehousing technologies are helping “bring data to life” for governments, citizens and businesses. I’ve also thought of some other ways this mobile/GPS data could create advantages:

* Businesses could examine historical patterns of people movement to decide where to open their next store/branch
* Real estate agents (commercial and residential) could use the data to determine migration patterns over time
* Businesses could examine the data to determine staffing and inventory levels by day or even hour based on historical traffic patterns in their vicinity
* Variable pricing could be enacted for access to roads or public transportation based on peak-demand usage (ex: toll roads into a city could charge more for drivers during peak hours much like London is doing)
* Chambers of commerce and city officials could use the data to steer promotions/traffic towards a new downtown renovation, places of interest, less frequently visited tourist attractions.
* Special events–mid-city concerts for example–can be modeled based on historical data of traffic, pedestrian flow etc, to ensure future events are more accessible

Through the use of powerful data-visualization applications, government agencies, businesses and citizens are able to explore data to uncover mathematical patterns and connections to help improve the lives of everyone concerned.

So, the next time you visit a major city, hit all the tourist attractions in a timely fashion, avoid the crowds and notice that trains, buses, and taxis run on time and are conveniently located, remember it’s probably not an accident. Good service rarely is.

What are your thoughts?

Could real time data visualization help improve the “citizen experience” in your city?

Are you concerned with privacy issues associated with the accumulation of mobile data?

Can you think of other ways that real time data from mobile providers can help citizens, businesses, and government officials drive efficiencies, cost savings, or increase revenues?

Land line and mobile operators–by nature–capture a significant amount of transactional data (call detail records, web visits/transactions, and GPS data just to name a few). To extract value from this data, companies employ data mining techniques.

Data mining is the process of discovering hidden patterns from large data sets. Using sophisticated algorithms, companies in all industries are “mining” mammoth data warehouses to identify useful information (relationships, rules, and sequences) that can help them customize and personalize offers, and optimize business processes.

However, according to a Technology Review article titled “Reality Mining,” MIT professor Sandy Pentland thinks mobile operators are poised to take data mining to a whole new level. Dubbed, “reality mining”, Dr. Pentland thinks mobile operators have an opportunity to record more than just where you’ve been or who you have recently called.

The article notes that Pentland “would like to see phones collect even more information about their users, recording everything from their physical activity to their conversational cadences.”

In the near future, Dr. Pentland suggests the following can be “learned” from studying data captured from your cell phone:

* Your cadence may reflect your state of mind that day …. are you happy, sad, depressed?
* Through capture of location based data, it will be possible to “predict” places you are more likely to visit
* Your calling patterns can help map your social network
* Your physical activity (or lack thereof) could be monitored by health professionals via your mobile device. Pedometer anyone?

The article continues, “Within the next few years, Pentland predicts, reality mining will become more common, thanks in part to the proliferation and increasing sophistication of cell phones. Many handheld devices now have the processing power of low-end desktop computers, and they can also collect more varied data, thanks to devices such as GPS chips that track location. And researchers such as Pentland are getting better at making sense of all that information.”

Of course, there are strong privacy considerations with the advent of these services. How does one opt in/or opt out? What information is shared and how much is shared and with whom?

Arguably, on the marketing side, more detailed information (including location based data), collected and analyzed by your wireless carrier could help them tailor and personalize specific offers–raising marketing effectiveness. And mapping your social network could help you share information more easily (think: favorite five plans–on steroids).
But there is a fine line between “benefit” and “big brother”.

• What do you think of the concept of “reality mining”?
• Would you be willing to opt-in to potential benefits of reality mining?
• Where would you “draw the line”?